用因果模型解释二值脉冲神经网络的决策，确保解释不包含无关特征。
Binary Spiking Neural Networks as Causal Models
- 将脉冲网络建模为二值因果系统，用逻辑方法分析其行为
- 通过SAT/SMT求解器找到像素级的反事实解释，准确率高
- 相比SHAP更可靠，避免引入完全无关的特征
我们对二值脉冲神经网络（BSNN）进行因果分析，以解释其行为。我们形式化定义了BSNN，并将其脉冲活动表示为二值因果模型。借助这一因果表示，我们能够利用基于逻辑的方法解释网络输出。具体而言，我们展示了可成功使用SAT和SMT求解器从该二值因果模型中计算反事实解释。为验证方法有效性，我们在标准MNIST数据集上训练了BSNN，采用基于SAT和SMT的方法，根据像素级特征寻找分类结果的反事实解释。同时，我们将所得解释与可解释人工智能领域常用的SHAP方法进行了对比。结果显示，与SHAP不同，我们的方法能保证所发现的解释中不包含完全无关的特征。
原文摘要 · Abstract (English)
We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally define a BSNN and represent its spiking activity as a binary causal model. Thanks to this causal representation, we are able to explain the output of the network by leveraging logic-based methods. In particular, we show that we can successfully use a SAT as well as a SMT solver to compute abductive explanations from this binary causal model. To illustrate our approach, we trained the BSNN on the standard MNIST dataset and applied our SAT-based and SMT-based methods to finding abductive explanations of the network's classifications based on pixel-level features. We also compared the found explanations against SHAP, a popular method used in the area of explainable AI. We show that, unlike SHAP, our approach guarantees that a found explanation does not contain completely irrelevant features.
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